Assessing the efficiency of a pharmacokinetic-based algorithm for target-controlled infusion of ketamine in ponies

The objective of this study was to assess a pharmacokinetic algorithm to predict ketamine plasma concentration and drive a target-controlled infusion (TCI) in ponies. Firstly, the algorithm was used to simulate the course of ketamine enantiomers plasma concentrations after the administration of an intravenous bolus in six ponies based on individual pharmacokinetic parameters obtained from a previous experiment. Using the same pharmacokinetic parameters, a TCI of S-ketamine was then performed over 120 min to maintain a concentration of 1 microg/mL in plasma. The actual plasma concentrations of S-ketamine were measured from arterial samples using capillary electrophoresis. The performance of the simulation for the administration of a single bolus was very good. During the TCI, the S-ketamine plasma concentrations were maintained within the limit of acceptance (wobble and divergence <20%) at a median of 79% (IQR, 71-90) of the peak concentration reached after the initial bolus. However, in three ponies the steady concentrations were significantly higher than targeted. It is hypothesized that an inaccurate estimation of the volume of the central compartment is partly responsible for that difference. The algorithm allowed good predictions for the single bolus administration and an appropriate maintenance of constant plasma concentrations.

[Acute lower gastrointestinal bleeding--an evidence-based algorithm for diagnosis and treatment]

Novel means to locate and treat lower gastrointestinal bleeding (lGB) allow to reduce the rate of required surgical interventions and help to limit the extend of resection. The risk stratification of patients with lGB is the primary step of our recommended treatment algorithm. Accordingly, risk stratifying instruments, which are only partly validated up to now, are gaining significance in lGB. Whereas, gastro-duodenoscopy and colonoscopy prior to angiography or scintigraphy are established diagnostic tools, capsule enteroscopy offers a novel approach to hemodynamic stable patients with lGB that are difficult to localize. With its every increasing sensitivity, Angio-Computer Tomography is likely to replace scintigraphy and diagnostic angiography in the very near future. In addition, recent advances in superselective microembolisation have been shown to have the potential rendering surgical interventions in a majority of patients with acute lGB unnecessary. The extend of required surgical resection is largely dependent on the success to localize the bleeding source of prior diagnostics. Only if the source is identified, a limited segmental resection should be performed. Should surgery be required, we suggest to maintain the effort to localize the bleeding, either by prior laparoscopy and/or by intraoperative entero-colonoscopy. Eventually, if the source of bleeding remains unclear total colectomy with ileorectal anastomosis represents the procedure of choice in patients with acute lGB.

A Hybrid Multimodal Non-Rigid Registration of MR Images Based on Diffeomorphic Demons

n this paper we present a novel hybrid approach for multimodal medical image registration based on diffeomorphic demons. Diffeomorphic demons have proven to be a robust and efficient way for intensity-based image registration. A very recent extension even allows to use mutual information (MI) as a similarity measure to registration multimodal images. However, due to the intensity correspondence uncertainty existing in some anatomical parts, it is difficult for a purely intensity-based algorithm to solve the registration problem. Therefore, we propose to combine the resulting transformations from both intensity-based and landmark-based methods for multimodal non-rigid registration based on diffeomorphic demons. Several experiments on different types of MR images were conducted, for which we show that a better anatomical correspondence between the images can be obtained using the hybrid approach than using either intensity information or landmarks alone.

[Osteoporosis - whom to treat? The importance of FRAX® in Switzerland]

The WHO fracture risk assessment tool FRAX® is a computer based algorithm that provides models for the assessment of fracture probability in men and women. The approach uses easily obtained clinical risk factors (CRFs) to estimate 10-year probability of a major osteoporotic fracture (hip, clinical spine, humerus or wrist fracture) and the 10-year probability of a hip fracture. The estimate can be used alone or with femoral neck bone mineral density (BMD) to enhance fracture risk prediction. FRAX® is the only risk engine which takes into account the hazard of death as well as that of fracture. Probability of fracture is calculated in men and women from age, body mass index, and dichotomized variables that comprise a prior fragility fracture, parental history of hip fracture, current tobacco smoking, ever long-term use of oral glucocorticoids, rheumatoid arthritis, other causes of secondary osteoporosis, daily alcohol consumption of 3 or more units daily. The relationship between risk factors and fracture probability was constructed using information of nine population-based cohorts from around the world. CRFs for fracture had been identified that provided independent information on fracture risk based on a series of meta-analyses. The FRAX® algorithm was validated in 11 independent cohorts with in excess of 1 million patient-years, including the Swiss SEMOF cohort. Since fracture risk varies markedly in different regions of the world, FRAX® models need to be calibrated to those countries where the epidemiology of fracture and death is known. Models are currently available for 31 countries across the world. The Swiss-specific FRAX® model was developed very soon after the first release of FRAX® in 2008 and was published in 2009, using Swiss epidemiological data, integrating fracture risk and death hazard of our country. Two FRAX®-based approaches may be used to explore intervention thresholds. They have recently been investigated in the Swiss setting. In the first approach the guideline that individuals with a fracture probability equal to or exceeding that of women with a prior fragility fracture should be considered for treatment is translated into thresholds using 10-year fracture probabilities. In that case the threshold is age-dependent and increases from 16 % at the age of 60 ys to 40 % at the age of 80 ys. The second approach is a cost-effectiveness approach. Using a FRAX®-based intervention threshold of 15 % for both, women and men 50 years and older, should permit cost-effective access to therapy to patients at high fracture probability in our country and thereby contribute to further reduce the growing burden of osteoporotic fractures.

EPA-1380 – Coordinated body-movement reflects psychotherapy outcome

Social interaction is a core aspect of human life that affects individuals’ physical and mental health. Social interaction usually leads to mutual engagement in diverse areas of mental, emotional, physiological and physical activity involving both interacting persons and subsequently impacting the outcome of interactions. A common approach to the analysis of social interaction is the study of the verbal content transmitted between sender and receiver. However, additional important processes and dynamics are occurring in other domains too, for example in the area of nonverbal behaviour: In a series of studies, we have looked at nonverbal synchrony – the coordination of two persons’ movement patterns – and it‘s association with relationship quality and with the outcome of interactions. Using a computer-based algorithm (Motion Energy Analysis, MEA: Ramseyer & Tschacher, 2011), which automatically quantifies a person‘s body-movement, we were able to objectively calculate nonverbal synchrony in a large number of dyads interacting in various settings. In a first step, we showed that the phenomenon of nonverbal synchrony exists at a level that is significantly higher than expected by chance. In a second step, we ascertained that across different settings – including patient-therapist dyads and healthy dyads – more synchronized movement was associated with better relationship quality and better interactional outcomes. The quality of a relationship is thus embodied by the synchronized movement patterns emerging between partners. Our studies suggest that embodied cognition is a valuable approach to research in social interaction, providing important clues for an improved understanding of interaction dynamics.

Differentiation of Diabetic Macular Edema From Pseudophakic Cystoid Macular Edema by Spectral-Domain Optical Coherence Tomography.

PURPOSE: To differentiate diabetic macular edema (DME) from pseudophakic cystoid macular edema (PCME) based solely on spectral-domain optical coherence tomography (SD-OCT). METHODS: This cross-sectional study included 134 participants: 49 with PCME, 60 with DME, and 25 with diabetic retinopathy (DR) and ME after cataract surgery. First, two unmasked experts classified the 25 DR patients after cataract surgery as either DME, PCME, or mixed-pattern based on SD-OCT and color-fundus photography. Then all 134 patients were divided into two datasets and graded by two masked readers according to a standardized reading-protocol. Accuracy of the masked readers to differentiate the diseases based on SD-OCT parameters was tested. Parallel to the masked readers, a computer-based algorithm was established using support vector machine (SVM) classifiers to automatically differentiate disease entities. RESULTS: The masked readers assigned 92.5% SD-OCT images to the correct clinical diagnose. The classifier-accuracy trained and tested on dataset 1 was 95.8%. The classifier-accuracy trained on dataset 1 and tested on dataset 2 to differentiate PCME from DME was 90.2%. The classifier-accuracy trained and tested on dataset 2 to differentiate all three diseases was 85.5%. In particular, higher central-retinal thickness/retinal-volume ratio, absence of an epiretinal-membrane, and solely inner nuclear layer (INL)-cysts indicated PCME, whereas higher outer nuclear layer (ONL)/INL ratio, the absence of subretinal fluid, presence of hard exudates, microaneurysms, and ganglion cell layer and/or retinal nerve fiber layer cysts strongly favored DME in this model. CONCLUSIONS: Based on the evaluation of SD-OCT, PCME can be differentiated from DME by masked reader evaluation, and by automated analysis, even in DR patients with ME after cataract surgery. The automated classifier may help to independently differentiate these two disease entities and is made publicly available.

Multi-modal diffeomorphic demons registration based on point-wise mutual information

In this paper we propose a variational approach for multimodal image registration based on the diffeomorphic demons algorithm. Diffeomorphic demons has proven to be a robust and efficient way for intensity-based image registration. However, the main drawback is that it cannot deal with multiple modalities. We propose to replace the standard demons similarity metric (image intensity differences) by point-wise mutual information (PMI) in the energy function. By comparing the accuracy between our PMI based diffeomorphic demons and the B-Spline based free-form deformation approach (FFD) on simulated deformations, we show the proposed algorithm performs significantly better.

An insulin infusion advisory system based on autotuning nonlinear model-predictive control

This paper aims at the development and evaluation of a personalized insulin infusion advisory system (IIAS), able to provide real-time estimations of the appropriate insulin infusion rate for type 1 diabetes mellitus (T1DM) patients using continuous glucose monitors and insulin pumps. The system is based on a nonlinear model-predictive controller (NMPC) that uses a personalized glucose-insulin metabolism model, consisting of two compartmental models and a recurrent neural network. The model takes as input patient's information regarding meal intake, glucose measurements, and insulin infusion rates, and provides glucose predictions. The predictions are fed to the NMPC, in order for the latter to estimate the optimum insulin infusion rates. An algorithm based on fuzzy logic has been developed for the on-line adaptation of the NMPC control parameters. The IIAS has been in silico evaluated using an appropriate simulation environment (UVa T1DM simulator). The IIAS was able to handle various meal profiles, fasting conditions, interpatient variability, intraday variation in physiological parameters, and errors in meal amount estimations.

A multifactorial analysis of obesity as CVD risk factor: use of neural network based methods in a nutrigenetics context

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.

Geometry-Aware Multiscale Image Registration via OBBTree-Based Polyaffine Log-Demons

Non-linear image registration is an important tool in many areas of image analysis. For instance, in morphometric studies of a population of brains, free-form deformations between images are analyzed to describe the structural anatomical variability. Such a simple deformation model is justified by the absence of an easy expressible prior about the shape changes. Applying the same algorithms used in brain imaging to orthopedic images might not be optimal due to the difference in the underlying prior on the inter-subject deformations. In particular, using an un-informed deformation prior often leads to local minima far from the expected solution. To improve robustness and promote anatomically meaningful deformations, we propose a locally affine and geometry-aware registration algorithm that automatically adapts to the data. We build upon the log-domain demons algorithm and introduce a new type of OBBTree-based regularization in the registration with a natural multiscale structure. The regularization model is composed of a hierarchy of locally affine transformations via their logarithms. Experiments on mandibles show improved accuracy and robustness when used to initialize the demons, and even similar performance by direct comparison to the demons, with a significantly lower degree of freedom. This closes the gap between polyaffine and non-rigid registration and opens new ways to statistically analyze the registration results.

Personalized X-Ray Reconstruction of the Proximal Femur via Intensity-Based Non-rigid 2D-3D Registration

This paper presents a new approach for reconstructing a patient-specific shape model and internal relative intensity distribution of the proximal femur from a limited number (e.g., 2) of calibrated C-arm images or X-ray radiographs. Our approach uses independent shape and appearance models that are learned from a set of training data to encode the a priori information about the proximal femur. An intensity-based non-rigid 2D-3D registration algorithm is then proposed to deformably fit the learned models to the input images. The fitting is conducted iteratively by minimizing the dissimilarity between the input images and the associated digitally reconstructed radiographs of the learned models together with regularization terms encoding the strain energy of the forward deformation and the smoothness of the inverse deformation. Comprehensive experiments conducted on images of cadaveric femurs and on clinical datasets demonstrate the efficacy of the present approach.

Treatment algorithm for moderate to severe ulcerative colitis

The care for a patient with ulcerative colitis (UC) remains challenging despite the fact that morbidity and mortality rates have been considerably reduced during the last 30 years. The traditional management with intravenous corticosteroids was modified by the introduction of ciclosporin and infliximab. In this review, we focus on the treatment of patients with moderate to severe UC. Four typical clinical scenarios are defined and discussed in detail. The treatment recommendations are based on current literature, published guidelines and reviews, and were discussed at a consensus meeting of Swiss experts in the field. Comprehensive treatment algorithms were developed, aimed for daily clinical practice.

The Bernese Hypothermia Algorithm: a consensus paper on in-hospital decision-making and treatment of patients in hypothermic cardiac arrest at an alpine level 1 trauma centre

INTRODUCTION: Guidelines for the treatment of patients in severe hypothermia and mainly in hypothermic cardiac arrest recommend the rewarming using the extracorporeal circulation (ECC). However,guidelines for the further in-hospital diagnostic and therapeutic approach of these patients, who often suffer from additional injuries—especially in avalanche casualties, are lacking. Lack of such algorithms may relevantly delay treatment and put patients at further risk. Together with a multidisciplinary team, the Emergency Department at the University Hospital in Bern, a level I trauma centre, created an algorithm for the in-hospital treatment of patients with hypothermic cardiac arrest. This algorithm primarily focuses on the decision-making process for the administration of ECC. THE BERNESE HYPOTHERMIA ALGORITHM: The major difference between the traditional approach, where all hypothermic patients are primarily admitted to the emergency centre, and our new algorithm is that hypothermic cardiac arrest patients without obvious signs of severe trauma are taken to the operating theatre without delay. Subsequently, the interdisciplinary team decides whether to rewarm the patient using ECC based on a standard clinical trauma assessment, serum potassium levels, core body temperature, sonographic examinations of the abdomen, pleural space, and pericardium, as well as a pelvic X-ray, if needed. During ECC, sonography is repeated and haemodynamic function as well as haemoglobin levels are regularly monitored. Standard radiological investigations according to the local multiple trauma protocol are performed only after ECC. Transfer to the intensive care unit, where mild therapeutic hypothermia is maintained for another 12 h, should not be delayed by additional X-rays for minor injuries. DISCUSSION: The presented algorithm is intended to facilitate in-hospital decision-making and shorten the door-to-reperfusion time for patients with hypothermic cardiac arrest. It was the result of intensive collaboration between different specialties and highlights the importance of high-quality teamwork for rare cases of severe accidental hypothermia. Information derived from the new International Hypothermia Registry will help to answer open questions and further optimize the algorithm.